Journal of Advanced Transportation最新文献

{"title":"A Review of Research on Flight Delay Propagation: Current Situation and Prospect","authors":"N. Li,&nbsp;H. G. Yao","doi":"10.1155/atr/4851103","DOIUrl":"https://doi.org/10.1155/atr/4851103","url":null,"abstract":"<div>\u0000 <p>The correlation between flight routes makes the initial delays easy to cause chain delays of downstream airports, forming a large area of delays. Studying delay propagation can block its propagation path and alleviate large-scale delay problems. This paper first systematically collects relevant academic literature from the past decade and constructs a co-occurrence keyword network to analyze and reveal the research hotspots in flight delay propagation. Then, based on the two major categories of delay propagation within airlines and between airports, this paper provides a detailed review and summary of their research methods. For the research of delay propagation within airlines, scholars mainly use econometric models, Bayesian networks, function models, and propagation trees to analyze the influencing factors and propagation characteristics of delay propagation. Among the methods for predicting delay propagation, models based on machine learning algorithms account for a large proportion and have shown good prediction performance. For the more complex delay propagation problem between airports, researchers mainly use the time interval Petri net, queuing network model, Cox proportional hazards model, and complex network theory to analyze the delay propagation mechanism in aviation networks. In addition, deep learning models and spatiotemporal network models have improved the accuracy of interairport flight delay prediction due to their ability to process large datasets and high-dimensional feature data. Finally, this paper summarizes the progress and shortcomings in flight delay propagation. The results show that there are significant differences in the delay propagation mechanism between airlines and airports, which requires full consideration of their applicability when selecting predictive models. Traditional machine learning methods perform well in the internal delay prediction of airlines, but there are some limitations in dealing with the complex and changeable propagation environment between airports. On the contrary, deep learning models and spatiotemporal models have opened up a new path for improving prediction accuracy by their powerful data processing and analysis capabilities. At the same time, researchers also need to constantly explore and optimize algorithms to overcome their current limitations and further improve the reliability of predictions.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/4851103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Effects of Built Environment on Traffic Microcirculation Performance Using XGBoost Model","authors":"Yuanyuan Guo,&nbsp;Feihong Li,&nbsp;Wumaieraili Aimaitikali","doi":"10.1155/atr/8821071","DOIUrl":"https://doi.org/10.1155/atr/8821071","url":null,"abstract":"<div>\u0000 <p>As the rapid motorization worldwide, the urban issue of traffic congestion continues to deteriorate. Among the solutions to address the traffic congestions issue, making full use of urban microcirculation roads so as to divert the traffic volume from main roads has been recognized as an efficient approach. However, few studies have explored what built environment determinants can affect the microcirculation performance and how do they correlate. By taking the central urban area in Tianjin, China, as a case study, his study aims to explore the nonlinear effect of urban built environment on the traffic microcirculation performance by using XGBoost model. The results show that it is observed with two troughs in traffic microcirculation performance by hours of a day, corresponding to the morning peak around 7:20–8:40am and evening peak around 16:00–19:00pm, respectively. Additionally, crossroad density is overwhelmingly dominant that affect the microcirculation performance, with an overall contribution approximately 40%. Moreover, one-way street, high-rise residential allocation, and T-intersection density are also the key determinants that contribute to the traffic microcirculation performance. Furthermore, most of the important built environment elements show a nonlinear relationship with the efficiency of traffic microcirculation, with slight difference between peak hours and off-peak hours. These findings can be used to collectively guide the local government to reasonably allocate the built environment elements so as to alleviate the traffic congestions.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8821071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Cascading Failure in Urban Metro Networks: A Dynamic Perspective Incorporating Changes in Travel Decisions","authors":"Huiying Lei,&nbsp;Xuedong Hua,&nbsp;Weijie Yu,&nbsp;Yongtao Zheng,&nbsp;Wei Wang","doi":"10.1155/atr/5576254","DOIUrl":"https://doi.org/10.1155/atr/5576254","url":null,"abstract":"<div>\u0000 <p>Accidents in urban subway networks often exert substantial and enduring impacts on urban transportation, drawing widespread attention from researchers. However, prevailing studies predominantly employ static methodologies to scrutinize incidents in urban subway systems, frequently assuming constancy in network topology and individual travel plans. Nonetheless, alterations in subway passenger flow also wield influence over network resilience. The manifold fluctuations in post-accident traffic flow and their repercussions on elastic transportation demand have hitherto been inadequately explored in the extant literature. Moreover, existing cascading failure models often lack precise definitions of scenarios, as in actual scenarios, passenger overflow following station closures can lead to cascading failures in subway networks. To bridge this gap, this study meticulously defines the research scenario, demarcates potential post-accident travel decisions, and analyzes the multifaceted factors influencing cascading failures in urban subway networks. Specifically, the proposed cascading failure model takes into consideration both the static characteristics of network topology and the fluctuations in passenger volume, along with their interplay. Additionally, when assessing station importance and quantifying network resilience, multiple travel decisions under different fault durations are duly considered. The study’s validation is conducted within the urban subway network of Nanjing, China. The findings suggest that accidents and fault stations of varied durations exert disparate impacts on passenger travel decisions, thereby influencing subway resilience. Notably, stations with integrated functionalities may hold more significant roles than those with singular functions. Furthermore, our results indicate a V-shaped effect concerning event duration—as the duration increases, the proportion of passengers canceling subway travel rises, thereby enhancing the likelihood of stabilizing the subway network. Consequently, this study, by incorporating realistic variations in travel decisions, furnishes a comprehensive understanding of the impact of events on urban subway networks. This dynamic perspective yields valuable insights for optimizing strategies in responding to events.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5576254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143831315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multitask Vehicle Signal Recognition With Dual-Speed Adaptive Weighting","authors":"Dianjing Cheng,&nbsp;Xiangyu Shi,&nbsp;Zhihua Cui,&nbsp;Xingyu Wu,&nbsp;Wenjia Niu","doi":"10.1155/atr/9961530","DOIUrl":"https://doi.org/10.1155/atr/9961530","url":null,"abstract":"<div>\u0000 <p>In mixed traffic environments, the accurate identification of vehicular devices’ modulation schemes, communication protocols, and emitter device information directly affects perception capabilities toward surrounding vehicles and infrastructure. However, existing studies predominantly focus on single-dimensional information analysis, resulting in limited completeness and accuracy in signal feature interpretation. This paper proposes a multitask learning framework (DSR-CNN-LSTM) for collaborative identification of this information. Furthermore, to mitigate task conflicts and noise interference, a dual-rate adaptive weight adjustment strategy is developed to optimize model performance through dynamic balancing of task learning rates and gradient update speeds. Experimental results demonstrate the superior performance of the DSR-CNN-LSTM framework in complex communication environments: Modulation recognition accuracy shows improvements of 20.67%, 10.38%, and 9.96% on three open-source datasets, while the weighted average recognition accuracy for communication protocols and emitter device information achieves enhancements of 45.52%, 72.21%, and 11.11%, respectively. The proposed model outperforms existing methods in both recognition precision and anti-interference capabilities, providing novel technical insights and solutions for the advancement of intelligent connected vehicle technologies.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/9961530","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on Small Target Detection Algorithm for Autonomous Vehicle Scenarios","authors":"Sheng Tian,&nbsp;Kailong Zhao,&nbsp;Lin Song","doi":"10.1155/atr/8452511","DOIUrl":"https://doi.org/10.1155/atr/8452511","url":null,"abstract":"<div>\u0000 <p>In recent years, road traffic object detection has gained prominence in areas such as traffic monitoring, autonomous driving, and road safety. Nonetheless, existing algorithms offer room for improvement, particularly when detecting distant or inherently small targets, such as vehicles and pedestrians, from camera perspectives. By addressing the detection accuracy issues associated with small targets, this study introduces the YOLOv5s-LGC detection algorithm. This model incorporates a multiscale feature fusion network and leverages the lightweight GhostNet module to reduce model parameters. Furthermore, the GC attention module is employed to mitigate background interference, thereby enhancing the average detection accuracy across all categories. Through data analysis, target detection at different scales and sampling rates is determined. Experiments indicate that the YOLOv5s-LGC model surpasses the baseline YOLOv5s in detection accuracy on the Partial_BDD100K and KITTI datasets by 3.3% and 1.6%, respectively. This improvement in locating and classifying small targets presents a novel approach for applying object detection algorithms in road traffic scenarios.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/8452511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Improved Kernelized Correlation Filter for Extracting Traffic Flow in Satellite Videos","authors":"Dudu Guo,&nbsp;Hongbo Shuai,&nbsp;Jie Zhang,&nbsp;Yang Wang,&nbsp;Miao Sun","doi":"10.1155/atr/2728376","DOIUrl":"https://doi.org/10.1155/atr/2728376","url":null,"abstract":"<div>\u0000 <p>In satellite video vehicle tracking, due to the tracking failure and tracking loss caused by similar characteristics of the target and obstacle occlusion, respectively, the traffic flow extraction accuracy is reduced. To address these issues, an improved traffic flow extraction method for satellite video based on kernelized correlation filter (KCF) was proposed. First, we introduced a multifeature fusion strategy into the KCF based on the discrete Fourier transform (DFT) framework to enhance vehicle tracking accuracy and reduce tracking drift and jumps. Second, we utilized the Kalman filter for trajectory prediction to reduce the loss of target during vehicle tracking. Compared with other mainstream algorithms on the satellite video dataset, the results showed that the tracking accuracy and success rate of the proposed method reached 86.74% and 79.96%, respectively. Finally, the virtual detection line method was used to extract the traffic flow. The experimental results showed that compared with the real traffic flow data obtained by visual method, the accuracy of satellite video traffic flow extraction by virtual detection line was 98.48% under noncongestion condition and 90.18% under congestion condition.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/2728376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Induced Traffic Volume Prediction of a New Highway Based on the ESE Gravity Model","authors":"Yifan Li,&nbsp;Mengmeng Zhang","doi":"10.1155/atr/1189526","DOIUrl":"https://doi.org/10.1155/atr/1189526","url":null,"abstract":"<div>\u0000 <p>Induced traffic volume is an important indicator for evaluating the feasibility of traffic project construction and predicting traffic demand. However, among the many models used in engineering to predict induced traffic volume, the economic spillover effects of new projects are not considered. To enhance the rationality of induced traffic volume prediction and to refine the prediction model, this study takes into account the economic spillover effects based on an analysis of the factors influencing induced traffic volume. Based on models of accessibility and regional economic potential, an economic spillover effect gravity model is proposed that considers the impact of the economy on induced traffic volume. The prediction results are more sensitive to economic factors and can reflect the driving role of the economy in regional traffic.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/1189526","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143801704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intersection Complexity Quantification Considering Driver Behavior Characteristics and Subjective Cognition","authors":"Fengxiang Guo,&nbsp;Lei Yang,&nbsp;Chang’an Xiong,&nbsp;Wenchen Yang,&nbsp;Wei Li,&nbsp;Yiwen Zhou","doi":"10.1155/atr/6161135","DOIUrl":"https://doi.org/10.1155/atr/6161135","url":null,"abstract":"<div>\u0000 <p>Intersections with high complexity often present an increased risk of accidents, thereby reducing traffic safety. Current models for measuring intersection complexity primarily focus on objective factors that influence intersection operation. However, they fail to consider the impact of intersection complexity on driver behavior or the feedback mechanism drivers exhibit in response to complex traffic environments at intersections. This study aims to investigate the intrinsic connection between driver behavior and intersection complexity. A real-vehicle experiment was conducted using three two-phase signal-controlled level intersections, each varying in objective complexity. Data on seven indices related to driver behavior characteristics and subjective cognition were collected from 28 participants during the experiment. Two methods were employed to analyze the data: (1) a descriptive analysis of driving behavior characteristics under varying levels of intersection complexity and (2) an entropy-object topologically comprehensive evaluation method for measuring two-phase intersection complexity based on driver behavior characteristics and subjective cognition. The results indicated that (1) drivers’ subjective perceptions of the complexity of two-phase signal-controlled intersections significantly differed from the calculated objective complexity, (2) differences in the effects of varying signal transition methods on driver behavior at complex intersections were not statistically significant, and (3) a two-phase intersection complexity measurement model based on driver behavior characteristics and subjective perceptions was developed and validated. These findings contribute to understanding the intrinsic relationship between driver behavior and intersection complexity in urban settings. Future research could integrate intelligent algorithms to enhance the safety of autonomous vehicles navigating intersections.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/6161135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solving the Electric Share-A-Ride Problem Using a Hybrid Variable Neighborhood Search Algorithm","authors":"Vincent F. Yu,&nbsp;Sy Hoang Do,&nbsp;Pham Tuan Anh,&nbsp;Cheng-Ta Yeh","doi":"10.1155/atr/6687585","DOIUrl":"https://doi.org/10.1155/atr/6687585","url":null,"abstract":"<div>\u0000 <p>This paper extends the share-a-ride problem (SARP) by incorporating electric vehicles (EVs) to reduce greenhouse gas (GHG) emissions, thus addressing environmental concerns. We introduce this new extension as the electric share-a-ride problem (E-SARP). We aim to generate E-SARP routing plans where EVs serve all passenger and parcel requests while visiting charging stations (CSs) as necessary for recharging. The objective is to maximize total profit from fulfilling passenger and parcel requests. We develop a mixed-integer programming (MIP) model and propose a hybrid algorithm based on the variable neighborhood search (VNS) framework, integrated with a simulated annealing (SA) acceptance criterion (HVNS). The MIP model provides optimal solutions for small E-SARP instances using the CPLEX solver, while the HVNS algorithm is designed to solve larger E-SARP instances. Numerical experiments are conducted to assess the performance of the proposed HVNS and to provide managerial insights, demonstrating that the use of EVs can effectively address environmental concerns without significantly compromising the profitability of the transportation network.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/6687585","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143793334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study of Mixed Platooning Considering Driver Perceptual Uncertainty","authors":"Junfeng Jiang,&nbsp;Yikang Rui,&nbsp;Bin Ran","doi":"10.1155/atr/5366331","DOIUrl":"https://doi.org/10.1155/atr/5366331","url":null,"abstract":"<div>\u0000 <p>Human-driven vehicles (HDVs) are the most critical element in mixed platoon research for their uncertainty. This paper presents a novel car-following model that considers the driver’s perceived uncertainty. A mixed platoon model of HDVs and connected and autonomous vehicles (CAVs) is established. Through data analysis, the stability of this model is validated. Additionally, a meticulous comparison and analysis regarding the platoon convergence ability and stable state under various platoon grouping forms and running speeds are carried out. Further, this paper introduces a virtual spring strategy to describe the car-following relationship between mixed platoon vehicles. Numerical simulations are then employed to explore the anti-interference capabilities of different mixed platoon modes and lengths. The results indicate that CAVs can effectively attenuate the randomness of HDVs. The platoon formation and operating speed impact the stability of mixed vehicle platoons. The platooning configuration “1 + <i>n</i> + 1” as the smallest platooning unit can help mixed vehicle platoons achieve a better stable state more quickly, with the optimal platoon length being four vehicles. However, as the platoon combinations grow more complex, the optimal platooning unit tends to shorten.</p>\u0000 </div>","PeriodicalId":50259,"journal":{"name":"Journal of Advanced Transportation","volume":"2025 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/atr/5366331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}